Zeitgeist

Zeit·geist = spirit, essence of a particular time

A collection of food-for-thought posts and articles on technology, business, leadership and management. 

Machine Learning: an industry perspective


by Garret Robertson - Senior Analyst & Author

contributors:

Satyajeet Salvi
Ruben Ramirez
Ellen Chan

The future of technology is in machine learning. Talk of virtual assistants, neural networks and deep learning is proliferating across the Internet at a rapid pace. According to a recent CB Insights update, deal flow in this space is accelerating rapidly with current estimates of the industry size exceeding $100 billion with compounded annual growth estimated at over 50%. Despite the proliferation of this technology, it is misunderstood. Dreams of androids, self-driving cars and Skynet abound in the conversations of executives, the general population and everyone in between.

Machine Learning industry size is exceeding $100 billion with compounded annual growth estimated at over 50%

Machine learning tools can more accurately described as powerful tools that sort through terabytes of data in order to optimize relationships. These tools find solutions for minimizing fraud, maximizing sales revenue, maximizing lead generation, or minimizing errors in image recognition. What makes these algorithms truly special is their ability to take complex structured and/or unstructured data and find meaningful relationships. Some examples of these data sources can include text heavy sources such as emails or web sites, images, audio files, and/or data points.

Despite lack of awareness, these tools are already finding places in consumers’ lives. When consumers log into Netflix at night and pick a show from the recommended play list or when they choose to add a recommended product to their basket on Amazon, machine-learning algorithms are at the heart of those lists. It is not just limited to product recommendation though, when consumers’ credit cards deactivate over suspicious transactions, there was machine learning. When Social media presents ads to users, there again was machine learning. Additionally these powerful algorithms drive other services like the virtual assistants Siri, Cortana or Alexa. While these examples may be visible to consumers, Machine Learning is rapidly proliferating into many less visible markets like CRM, healthcare and government services and banking.

The valuation of machine learning service companies can best be described by its synergies with cloud service providers and businesses. Businesses create systems that gather data as they conduct business. These systems could include, as an example, systems for tracking customer receipts like an accounting ledger or a customer-profiling tool like a rewards program. Data science showed businesses how to combine these two data sets to better understand customer preferences. When the data moved to cloud services, machine-learning tools were then able to sift through much more complicated information like images, articles, or other unstructured sources and automate the search for interesting relationships.

The valuation of machine learning service companies can best be described by its synergies with cloud service providers

As the outputs became better, the businesses rebuilt systems to integrate more data necessitating more data storage. Now the systems could create profiles, link them to purchasing trends and compare it to even more complex demographic information creating more powerful business insights. The outputs from the 3-way cycle thus reinforce themselves making it more and more efficient and increasing value to all parties.

These synergies define how the industry has been growing. Because the synergies are so strong, most capital investments in this industry occur as partnerships between businesses, cloud service providers and machine learning companies. These strategic investment partners provide two critical pieces to the growth round. First, they validate the effectiveness of the machine-learning product. Second, these partnerships provide access to data from interesting industries such as fraud, healthcare, product recommendation or sales analytics allowing opportunity for the systems to become even more effective.

most capital investments in this industry occur as partnerships between businesses, cloud service providers and machine learning companies

Below is a sampling of some capital raises for machine learning companies where at least one of the investors was not a capital player but a business with a strategic interest and/or a cloud service provider.

The application of this technology is expanding every day. Nearly 70% of all investment into this space is driven by Seed and Series A funding. Additionally more than 40% of all companies that exist in this space are less than 3 years old. Additionally, with the power these solutions have to offer, the industry is expanding rapidly with total year over year transaction and investment volume increasing.

Nearly 70% of all investment into this space is driven by Seed and Series A funding
More than 40% of all companies that exist in this space are less than 3 years old

Due to the synergies in this industry, a few companies have been able to lead the charge. Some of these companies include Amazon, Microsoft, Google, IBM and Apple. This makes sense because the effectiveness of the algorithms grows as the access to relevant data grows. Companies with access to large quantities of data find more value than those with less.

Despite the power of machine learning, there remain two important hurdles for the typical company in adopting these technologies. First, company leadership needs to be aware of how these systems can help them. Understanding how data can be used to redefine and refine existing strategies is crucial in transforming the organization’s systems. General misunderstanding of machine learning has prevented many companies from adopting it.

The effectiveness of the algorithms grows as the access to relevant data grows

Second, if companies want to pursue implementation of these systems, they need to understand how. This involves not only utilizing tools to gather the data, but also knowing what kinds of solutions are already available.

There are many machine learning companies such as BigML, Amazon, IBM, Microsoft, Google or others that have out-of-the-box solutions available to a wide range of industries. Increasingly, machine learning is moving from the world of PhD’s and large teams of data scientists to tools that anyone can implement.

Despite the newness of this technology to businesses, many industries have already found interesting and powerful solutions. A summary of some industries that have been impacted by machine learning as well as some specific examples in selected industries follows.

Retail/Ecommerce:


This is one area where the use of machine learning is most visible to consumers. When customers buy products online, they leave behind with the business a treasure trove of information. Some of this information includes what products are typically bought together, how much the average consumer spends in a given purchase, what sorts of products and brands people like and much more. While individual tickets report single transaction information, registered users create entire shopping profiles over multiple purchases that can be analyzed.

With this kind of information, it is no wonder that Amazon reported shortly after rolling out its product recommendation platform that sales increased by nearly 30%. In fact this is not an uncommon story. With companies better able to identify the needs and wants of users, they are better able to put products consumers want, into their hands.

In addition to product recommendation, chatbots are taking over customer service. More than 11,000 bots have been added to Facebook Messenger since its launch, allowing brands and companies to use AI to connect with customers through virtual concierge services. These bots are replacing employees in physical stores, allowing companies to build long-term relationships with customers while saving labor costs.

Spring Bot is one example of many of these services that acts as a point of contact even after purchases are made and has a wide range of customers, including Givenchy and Lanvin, brands that do not have an established e-commerce platform. An automated interaction generally costs $0.25, while a live agent interaction costs anywhere from $6 to $20. The automated interactions are also faster than normal live interactions. While the natural language processing in these systems is not perfect, the overall results speak for themselves.

Sales/CRM:

Increasingly machine learning tools are being used to enhance sales and CRM. Traditionally, sales data has been stored and analyzed manually. In addition to the time and money spent in performing these tasks, significant capital has been spent training sales teams to track the right data and how to effectively analyze it.

Machine learning has provided a way to collect data automatically and provide the analysis so sales agents can more effectively find, target and convert prospective clients into sales. InsideSales reports that some of its customers have increased their sales pipeline by 30% increase to sales and a 250% increase in leads. Costs associated with training implementation and data entry are reduced for users in addition to these strong revenue increases.

Financial Services:


Increasingly, financial institutions are using automated financial advisors and planners. These tools monitor events and stock and bond price trends and compare them to the user’s financial goals. The machine will be able to compare the user’s portfolio and make recommendations on what stocks to buy or sell. There will be no need to pay an expensive human advisor to make decisions for customers. The machine-learning tool will now be able to make decisions based on data that is coming in real time.

In addition to automated financial advisors, algorithmic trading is a means to increase profitability and decrease risk in investment portfolios. Algorithmic trading systems are systems that process data on a very large scale to identify risks in investment portfolios and rebalance them in order to minimize risk. As these systems gain more data, they are better able to optimize portfolios and mitigate risk.

Alg-trading:

It is estimated that these algorithmic trading systems handle 75% of the volume of the global trades worldwide. These numbers get larger when looking at specific types of trading.

Algorithmic trading systems were responsible for nearly 80 per cent of foreign exchange futures trading volume, 67 per cent of interest rate futures volume, 62 per cent of equity futures volume, 47 per cent of metals and energy futures volume, and 38 per cent of agricultural product futures volume between October 2012 and October 2014.

Healthcare:


Clinical variation management is an area in healthcare ripe for disruption by ML systems. Clinical variation is when clinicians deviate from recommended care pathways in the delivery of care to similar patients. It is estimated that there could be as much as 30% waste in healthcare, but this waste is hard to identify due to the complexity of healthcare and the great degree of variation in the way patients receive treatments.

A recent article by HealthCatalyst indicates the problem. Clinical variation is complicated by two main factors. The first is that studies indicate that only 20% of the care delivery is driven by scientific research. About 80% of the care delivery is determined by subjective clinical care pathway decisions. Second, Doctors must read hundreds of pages of primary literature every day in order to stay fully current. The process needs education, but the education is next to impossible to get and train through normal means. Until then, clinicians deliver care without much consistency driving waste and impeding process development.

Machine learning provides a means to monitor care pathways to ensure clinical variation is minimized. It also provides a means to monitor care pathways to determine areas to improve and optimize them with current methods in mind.

Traditional tools such as control charts, regressions and manually examined data are not robust enough to optimize the system. Machine learning tools are well positioned to do the work individual data scientists and analysts cannot do. Those machine-learning companies focused on healthcare like Ayasdi are well positioned to disrupt this space.

Concluding Remarks


  • Machine learning as an industry is still in its infancy.
  • These examples represent only a few of the hundreds of companies that are emerging to solve next generation business problems.
  • A new industrial revolution is coming in the form of computer code and automated data science.
  • Companies that are not thinking about data and machine learning will soon find themselves unprepared.
  • The companies who have adopted these technologies already enjoy significant advantages over those who have not implemented it yet. **

*special note of thanks to Naiss' contributors:

Satyajeet Salvi
Ruben Ramirez
Ellen Chan

References:

http://venturebeat.com/2016/08/16/machine-intelligence-2-0-in-charts-and-graphs/

http://www.businessinsider.com/investors-are-backing-more-ai-startups-than-ever-before-2016-6

http://fortune.com/2012/07/30/amazons-recommendation-secret/

http://www.businessinsider.com/statistics-on-companies-that-use-ai-bots-in-private-and-direct-messaging-2016-5?r=UK&IR=T/#this-survey-is-based-on-31-million-private-messages-and-15-million-public-wall-posts-gathered-from-256-companies-1

http://www.springbot.com

https://www.insidesales.com/resources/customer-success/

https://www.healthcatalyst.com/role-clinical-variation-medical-practice

http://raconteur.net/technology/the-rise-of-ai-and-algorithms-in-the-financial-services-sector

http://fsroundtable.org/cto-corner-artificial-intelligence-use-in-financial-services/

https://www.atkearney.com/documents/10192/698536/FG-Big-Data-and-the-Creative-Destruction-of-Todays-Business-Models-4.png/dcfbce8d-1156-4b44-91a6-0e2d4fca6ce7?t=1358296795126

https://www.crunchbase.com/#/home/index

An End to Moore's Law: the Dawn of a New Computing Era

(continuation to: The Second Arms Race: Artificial Intelligence)

Many information technologies have evolved at exponential rate (Nagy et al, 2011), Moore’s law, stating the transistor count doubles every 2 years, has been at the core of causality for 50 years.

But this trend may not hold for much longer (Mack 2011, Lundstrom 2003) as per physical limitations of silicon, or, maybe we don’t see the forest for the trees.

•    There are limits to the exponential growth inherent in each paradigm. Moore’s law was not the first paradigm to bring exponential growth to computing, but rather the fifth. 

•    In the 1950s they were shrinking vacuum tubes to keep the exponential growth going and then that paradigm hit a wall. But the exponential growth of computing didn’t stop. 

•    It kept going, with the new paradigm of transistors taking over. Each time we can see the end of the road for a paradigm, it creates research pre, quest for the pressure to create the next one. 

 •    That’s happening now with Moore’s law, even though we are still about fifteen years away from the end of our ability to shrink transistors on a flat integrated circuit. 

•    We’re making dramatic progress in creating the sixth paradigm, which is three-dimensional (quantum) molecular computing. 

Ray Kurzweil – The Singularity is near

The dawn of a new computing era: More than Moore MtM

Moore’s law will come to an end as a consequence of physical limitations of silicon; three dimensional quantum computing is poised to take over as the new paradigm. 

Quantum computing timeline:


2013

   
•    Coherent superposition of an ensemble of approximately 3 billion qubits for 39 minutes at room temperature. The previous record was 2 seconds.


2014

    
•    Documents leaked by Edward Snowden confirm the Penetrating Hard Targets Project, by which the National Security Agency seeks to develop a quantum computing capability for cryptographic purposes.

•    Scientists transfer data by quantum teleportation over a distance of 10 feet (3.048 meters) with zero percent error rate, a vital step towards a quantum Internet.


2015

    
•    Optically addressable nuclear spins in a solid with a six-hour coherence time.

•    Quantum information encoded by simple electrical pulses.

•    Quantum error detection code using a square lattice of four superconducting qubits

 

Quantum computing promises to augment computing power a billion fold, however, we may not need to get there to develop a strong Artificial Intelligence, one that has the capacity to improve and evolve by itself.

The expectation is that soon after we reach a strong AI matching a human brain, the ability to replicate it rapidly and limitlessly will generate a self-improving general AI, which in turn would accelerate intelligence exponentially.

@efernandez

Next: An Explosion of Intelligence: The A.I. Arms Race

The Second Arms Race: Artificial Intelligence

The second arms race is actually the third. The first one was the naval race during World War I, followed by the Cold War between United States and the Soviet Union, scaling up nuclear weaponry right after the end of World War II. 
 
The human race has been able to manage the prisoner's dilemma inherent to these competitions so far, and now faces a new test with the advent of a new technology breakthrough: Artificial Intelligence.
 
This is a series of articles on the topic providing a vision toward an artificial intelligence explosion in the context of current economic changes supporting a shift in our economy toward an altruistic model. 

The Intelligence Explosion & the Singularity: 

The Arms Race in Artificial Intelligence & The 4th Sector of the Economy

Ed Fernandez @Efernandez Palo Alto. California.

Introduction:


  • Technological singularity seems plausible and recent advancements in machine learning and AI suggest the ‘intelligent explosion’ event is within reach in this century.

 

  • A n arms race of narrow AI entities will happen in the framework of today’s traditional economy. Strong intelligence or AGI will eventually emerge followed by an explosion of intelligence.

 

  • New globalization processes driven by technology are fueling the sharing economy, as well as the 4th sector where public, non-profit, social and mission oriented enterprises are converging.

  • The 4th sector is poised to grow and thrive enabled by the sharing and collaborative economy; mission driven enterprises will have more resources enabling them to play a key role shaping the right path for AI evolution.

  • The AI arms race will provide ‘good’ and ‘bad’ entities in the context of existing and new economy environments (traditional and altruistic economies)

 

  • We, humans, as a species, can succeed managing the risks of a superintelligence event as we did in the past overcoming other technology threats (i.e nuclear)

We have the capacity to anticipate the future with a certain degree of precision.  Our prediction accuracy is lower as we increase the time horizon we aim at.

It’s pretty straight forward for us to predict short term events, those more likely to impact our survival chances; mechanical or physical, like anticipating when a car is going to cross at the juncture we are on, or, more long term and qualitative, anything related to replicating our gene pool, for instance the chances to date a specific person of the opposed sex.

However, when we look further ahead in time, and, because of our brainpower limitations and the effort required we struggle to foresee all potential possibilities and combinations.

Our brains, during evolution, developed a pattern-based approach to efficiently solve this problem. Identifying patterns allow us to see the big picture of a possible future, although we remain unable to predict the smaller details within (stacking up to conform to the pattern).

The Singularity, as defined by Ray Kurzweil, arguably the biggest ambassador of this concept in our times, is a period of time in future where technological advances will evolve so rapidly (exponentially) that humanity will not be able to keep up with them.

This definition needs to be broad because is a concept coined after careful analysis of the evolution of many technologies. It looks into historical data and speed of change rather than specific events themselves (although Singularity is mostly associated to the dawn of a super-intelligence entity capable of self-improvement).

We say can’t see the forest for the trees referring to short term events clouding our ability to see the big picture. The opposite is true for forward-looking statements.

With sufficient historical data we can develop patterns (and see the forest) but we will remain clueless about details (trees).

To state the analogy, let’s have a look at a practical example, a piece of technology we are all very familiar with, our phones.

 

Wireless phones (smartphones) have undoubtedly been the protagonists of the technology revolution in recent times.

 

The way smartphone technology has been adopted is well described by the diffusion of innovations theory (Everett Rogers - 1962), expressed graphically by an S curve (logistic function) or the widely popular bell curve (derivative of the S curve).

The process is well documented using available data from smartphone manufacturers (sales of devices over time) to the point we can track and predict with a certain degree of accuracy what the future will be for this particular technology.

The graph for US smartphone adoption, now above 70% penetration (Horace Dediu – Asymco), follows accurately the bell curve pattern to the point we can predict overall sales volumes in the years to come (this would be the forest in our analogy), but we are unable to predict which manufacturer will get the greater share in the same way we couldn’t predict Apple’s iPhone explosive growth since 2007 (those are the trees).

Thus, in a competitive and evolutionary environment as the current economy creates, with sufficient historical data, these well known patterns allow us to anticipate how technology breakthroughs will penetrate the markets and impact the population as a social group.

Details (trees) remain hidden though. We can’t predict which species (corporations) will be winners or losers; however, the scope and length of the ‘race’, market size and time span can be forecasted with fair accuracy.

The social aspects of technology adoption, with increasing mobile computing and ubiquitous Internet, are shrinking adoption cycles.

The number of new technology breakthroughs is also increasing over time. The intuitive idea of a singular future with unlimited wonders driven by technology makes more sense than ever.

This vision has fuelled Sci Fi literature and movies since the 50ies. The concept of Singularity, a future time where technology outwits human capabilities, may be now perceived as stating the obvious, a self-fulfilling prophecy.

The question is when.

 

But, not so fast…. First, let’s ‘take a selfie’ of the present and look at today’s status quo.

 

@efernandez

 

Next: An End to Moore's Law [...]

The demise of the smartphone is inevitable, and necessary

ninos_ignorando_museo

 A shorter, edited & curated version of this article, published by CNBC.com on the 20th of May, 2015, is available here.

Big thanks to Eric Rosenbaum, CNBC strategic content editor, whose edits (and a notorious headline) made this article one of the top stories at CNBC.com and a social media hit the day of publishing (ranked among top 5 CNBC stories driving engagement)

The War is Over

Smartphones coupled with mobile services and apps (mobile ecosystems) have been the protagonists of the latest disruption tide for well over a decade. Horace Dediu is probably among the best analysts who have covered the phenomenon.

The Smartphone industry is a monumental business accounting for more than $380 Billions last year, on more than 1,2 Billion devices sold, according to IDC.

Furthermore, IDC is forecasting just under Half a Trillion dollars in revenues by 2018 ($451 Bn to be precise).

Despite these extraordinary numbersthis market has reached maturity and YoY growth is declining gradually, with manufacturers working with cut-throat margins and one single player monopolizing gainsseizing an estimated 93% of industry profits according to Cannacord.

No need to guess, just look around you, most likely you have one or more Apple devices on your desk or in your pockets.

Despite there are an estimated 8 Bn smartphones still to go into the market in the next 5 years, this industry is technically over.

...even in China.

AI-CH098_DIGIT_NS_20140305050304

Applying the diffusion of innovations theory (a.k.a the diffusion of technologies bell curve), when a technology goes over 50% penetration, the remaining audience is composed of a  late majority of followers and laggards.

In other words, with smartphone penetration well over 70% in more developed countries like US, the saturation point has been exceeded long time ago, and the 8 Bn shipments to happen in next 5 years are driven by emerging markets, less penetrated (hence rising star Xiaomi) and shorter product lifecycles with little incremental innovation (hence commoditization, profits diminishing for all manufacturers, hence Apple & others moving quickly into wearables).

Screen-Shot-2013-12-10-at-12-10-10.32.13-AM  

History repeating

The Smartphone war is over. I’ve been myself involved in the mobile industry for nearly two decades (with Nokia and BlackBerry). I started when there weren’t yet internet capable phones and GSM was just a promising standard in Europe.

This is what happened:

From a software perspective, Operating Systems turned competition into a mobile ecosystems war (a.k.a mobile apps & services war) which ended in a duopoly with Android capturing majority of volumes and iOS taking a lion share of the profits.

Before that, devices didn’t have enough computing power nor couldn’t deliver the user experience to drive adoption of content, apps and services (but, for the record, back to the future 15 years ago there was a world of app stores, mobile services and everything we have seen exploding in the smartphone era, and all of it was already working, it was simply not adopted or diffused widely)

Google’s android and Apple iOS disruptions were enabled by asymmetric business modelsApple profiting from HW margins (while investing heavily on an ever growing iOS ecosystem & apps), Google making money out of their services rendered through a myriad of devices running android (commoditizing the OS by giving Android AOSP for free).

Apple case is ironic, as hardware sales and iphone in particular is piggybacking on carriers and the telco services industry (an estimated 80% of iphone market relies on carrier subsidies). Telco (carriers) is a several trillions industry providing the underlying infrastructure and data connectivity over which both hardware (smartphones) and software (Apps & services) have grown explosively (a.k.a OTT services).

Services have been actually the disruptor element driving adoption, ultimately dragging sales of hardware with them (Apple is today’s example, BlackBerry was a pioneer with this asymmetric model).

In its early beginnings BlackBerry didn’t even have intentions to get into the hardware business, their offering was originally focused on the service side only. BlackBerry’s messaging proposition evolved into the incredibly popular mobile push email which Wall Street embraced. Utterly 'forcing' users to buy anti-fashion qwerty devices as a necessary 'accident' to have real time email. This was back in 2001-2005.

This asymmetric offering turned into a phenomenal hardware business for BlackBerry, fostered by carrier driven sales of push email services embedded in their data plans.

Same pattern follows Apple, building an incredible ecosystem of apps & services which in turn make users desire and buy the hardware devices, and it’s in hardware where the margins and profits lie.

Ok, we’re done with smartphones, what’s next?

 

In any industry, once maturity has been reached, it’s poised to disruption, typically even before arriving to the tipping point of the adoption bell curve. Clay Christensen innovation dilemma explains this.

In essence the reason why it is so difficult for existing firms to capitalize on disruptive innovations is that their processes and their business model that make them good at the existing business actually make them bad at competing for the disruption.

But, how is this disruption going to happen in the case of smartphones?

Think of smartphones as the entry point to the online world. Now, wouldn’t it be better, easier and more convenient to access your digital world without the constraints of a small screen?

Everything outside the realm of your smartphone’s touchscreen form the domain of disruption for this industry.

To put it bluntly, our heads can’t continue down staring to our screens. Something must be done to fix this, and, the basic technologies to do it are already there.

tumblr_mn49msj41n1r6rd7ko1_1280

The post-smartphone era is beautifully described by Horace Dediu in this post (a piece of poetry for analysts). 

The writing is in the wall

Early signs of what´s to come can be seen even embedded in our devices in certain ways already.

Siri, Cortana, Google Now are voice portals replacing screen access and typing. These are actually NLP (Natural Language Processing) and AI technologies combined in the cloud.

Smartphones have started talking and displaying information to TVs, projectors and now to smartwatches and wearables.

Furthermore, we have now smart-glasses and head mounted displays capable of displaying virtual images (AR/VR) blended with our natural view of the physical world (MS Hololens, Magic Leap, Oculus Rift). These devices can also understand gestures.

All indicates we will be using our voice instead of typing, and we will be interacting with images well outside the limitations of today’s smartphone screens.

Now, let’s recap what the smartphone wars taught us over the last decade, and, let’s couple it with the early signs of what’s to come:

  • Services are the enabler and differentiator driving hardware sales. (the interface and point of entry for the user is king, think search box or voice recognition)

  • The majority of profits come from Hardware sales (think iphone revenues, hence Apple smartwatch)

  • Smartphone industry is mature and poised to disruption (market is ready to accept new propositions)

  • The new disruption wave of services will be driven by virtual assistants operated by voice and gestures combined with virtual reality (digital images outside phone screens) running on new smart wearable/apparel hardware (again, think voice enabled interfaces, Siri, Cortana, Google Now as disruptors at interface level)

  

We can discern how new disruption devices will be. At the intersection of some sort of smart – eyeware with powerful Augmented Reality display and an advanced voice recognition capabilities, coupled with wireless earbuds, as well as with other wearable apparel equipped with sensors all over our body.

But more important than any of these pieces of hardware, (remember, services drive hardware adoption not the other way around), services in this new smart-wearable context will be delivered through the new access points, voice and gestures.

Access determines hardware but, the key element gluing all together and managing how humans interact with this new mobile computing platform is Artificial Intelligence.

Artificial Intelligence in the form of a guardian angel (yes, the movie Her is an excellent representation of this concept, otherwise refer to HAL the ill computer in 2001 Space Odissey).

If you google ‘virtual assistant’ you’ll get around 18M entries, and you’ll struggle browsing results endlessly to find even the first reference to a truly artificial virtual assistant. It means we are still far from a practical ‘HER’ like experience and for the time being, we are hiring human assistants by the hour to do the tasks, offshore.

Most likely, we will be flooded by wearables, smart glasses, apparel and all kind of fragmented technologies while the new AI powered, cloud based operating system, takes over control of human interaction with the world.

 stock-photo-the-guardian-angel-is-feeling-underappreciated-and-says-to-her-charge-let-me-guess-you-were-100107416

Whoever gets that AI guardian angel operating system to work seamlessly with humans, will disrupt the disruptors and will take control over the wearable hardware, which ultimately will need to bend to its (proprietary) specifications or be left out of the service proposition.

Jay Samit, author of Disrupt Yourself, said

“Disruption causes vast sums of money to flow from existing businesses and business models to new entrants”.

Let’s do a quick & dirty math, in the scenario we have pictured here, considering the smartphone industry represents an average of $350 Mn per year in revenues, there is potential to disrupt $1,750 Bn over the course of the next 5 years.

Big time for venture capitalists.

Fascinating times ahead, welcome to a brave new world of double back-flip disruption.

Dedicated to Graciela, my better half & lifelong soulmate, without whom I would be lost.

photofuna_HiUQCZMUoASw8KLfJY6oXA_r_edit

The Hitchhiker Guide to A.I.

Warning: this is a long article, almost 1500 words long.
A lighter, friendly edited version was published by CNBC 
on Friday, 27th of March, 2015. You can find it here.

'If there is Artificial Intelligence then we won’t need to think’ said my recently-turned-ten daughter, on our way driving her to a piano lesson.

She left me speechless while I was trying, in vain, to remove from my mind a sticky image from the movie ‘The Hitchhiker guide to the Galaxy’. A supercomputer called ‘Deep Thought’ which, after 7,5 million years of calculations rendered the answer to the Ultimate Question of Life, the Universe and Everything, or, namely, the number 42.

Quirky cult movies aside, the impact of artificial intelligence is immense, and the implications phenomenal.

Deep_Thought

An utopian-dystopian future:

Artificial Intelligence (A.I.) is on the hype again. There are dystopian, terminator-like views backed up with warnings from renowned scientists, Stephen Hawking to start with. Even Tesla and SpaceX founder Elon Musk appeared on CNBC’s ‘Closing Bell’ cautioning about the potential risks of this technology.

On the other side, Jeff Hawkins, scientist and serial entrepreneur (Palm, Handspring), founder of the Redwood Center for Theoretical Neuroscience, vehemently argues the Terminator is not coming. His company, Numenta, is reverse-engineering the human neocortex for AI purposes.

But, while an eventual life extinction event comes, we have families to care about and businesses to run so, what is this technology going to do to us in the short term and more importantly, as a CEO, what should I do about it?

noteworthy: this draft spun off another great post from Peter J. Simons on the topic, see here 'How Artificial Intelligence can boost Corporate Innovation'

Business impact, artificial intelligence today:

Firstly, for business purposes, we should think of Artificial Intelligence as a natural evolution of technology, enabling yet another disruption wave in markets, though, arguably this time, a much bigger one than the internet was.

Keeping over-hype and human destiny aside, CEOs and management at companies can think of AI as yet another technology disruption. An enormous, multipronged and pervasive one, like digitization and the internet was in the 90’s, or mobility and the smartphones were a decade ago.

First we had data and Databases (i.e Oracle, MySQL), then, back in the 80’s we combined data and infrastructure (Ethernet, Networks, TCP/IP). The Web was a first big data-base (HTML, HTTP), indexed by Google in the 90’s.

Fast forward to the new millennium, dawn of the modern Big Data (Hadoop, Cloudera, Palantir) and built on top, Analytics, thriving and striving to find meaningful insights and patterns in data through Algorithms.

These all evolving ultimately into predictive analytics and Machine Learning (also referred as intelligent applications and Artificial Intelligence).

And here we go, we all witnesses of a new era of Artificial Intelligence now ready for prime time.

(Machine learning is considered actually a branch of artificial intelligence, the other being computational logic & planning.)

 

datasciencebasedontrafficdatafinal 150217170119 conversion gate01.pdf

Embedded and in many cases unnoticed, today there are already thousands of narrow artificial intelligence applications in areas such as:

·         Natural Language Processing      (virtual assistants; i.e Siri)

·         Finance                                         (stock trading, credit score analysis)

·         Toys and games                            (from Furby to Aibo)

·         Publishing & writing                     (i.e computer generated news)

·         Robotics, Genetics, Research, Law, Transportation

Another way to size up intuitively the potential of AI technology is to look at its effects today, starting from those deriving from the good ‘old’ predictive analytics methods:

Using marketing analytics, Target got to know a teenage girl was pregnant before her own father did, isn’t it creepy?

 

According to Marissa Mayer credit card companies can predict a divorce two years before it happens with 98% accuracy, based solely on your purchase decisions.

High Frequency Trading, enabled by AI algorithms, caused the US stock market crash of 2010 in which the Dow Jones plunged 9% (1.000 points) within minutes. It was the biggest one-day point decline in the history of the Dow Jones. In that year an estimated 70% of all equity trading volume was done by machines.

data6

Companies in general can utilize analytics to predict customer sentiment (a.k.a. V.O.C. or Voice Of Customer). By analyzing unstructured data, from internal sources, databases, CRM as well as data from social networks and public sources, an organization can choose when to intervene and interact directly with customers. All to improve consumer engagement and brand perceptions.

Moreover, machine learning is now in a rapid democratization process. Google Prediction API for instance lets you build your own smart applications for sentiment analysis and purchase prediction, all from the cloud.

Ahead of Google APIs and IBM Watson, the most advanced AI developments today are coming from what is called deep reinforcement learning. Demis Hassabis’ Deep Mind group at Google is at the forefront of this research and the results are impressive:

Super-Breakout-2011-08-10-09-50-33-63[1]_0

Remember the old classic Atari games from the 80s? we were dragged into endless hours of fun and play to master Pong, Breakout or Space Invaders, right?. Now, an AI machine with no other input than raw pixels and a reinforcement learning algorithm is capable of over performing the most expert of human players.

In the future nearly everything connected to the internet will use a form of a machine learning algorithm in order to perform its mission.

And Yes, over the course of the following years and decades, A.I. will enable disruptions of the same and bigger magnitude than the internet did. Exciting times ahead for venture capitalists.

What Shall I do as a CEO?

In a simple visual flowchart:

A-CEOs-beginners-guide-to-AI

You may expect more and more Everything as a Service, from sourcing talent to getting probabilistic predictions of the sales of your products. Your company needs to keep up with the times, embrace new services, AI based technologies and benefit from them.

Similarly to what happened a decade ago, when enterprise mobility entered the strategic roadmap of all major corporations, AI enabled technologies will become an integral part of the strategy planning process in a near future.

You may think AI is not there, not in a conspicuous way at least, think twice, does your marketing people hire research or customer insights reports? most likely your contractors are using big data analytics to deliver their conclusions to you. Is your competitor doing so in-house? That may indicate they can react and move faster in decision making and eventually interact rapidly and more decisively with (your) customers.

In essence, you need to map out existing internal and external resources of your company and check if they are using any of these big data, analytics and AI related technologies and tools.

See here for detail, courtesy of Jose Luis Martinez Fernandez PhD & CTO at Daedalus S.A. an analytics and predictive technology company.

Plain simple, to monitor AI readiness of your company, first, earmark a tech champion in your management team (if there isn’t one already, the CIO promoted to CDO or CMO will do for the time being), and task her/him to screen out if any of these referred tools or technologies are used either internally or externally. If the answer is a hard no internally, run a supplier & partner appraisal to understand how far AI is from the core of your business.

Give yourself a 1 to 10 score in the lows and start pushing the company to embark in the AI journey (in the same you probably told your management team to start using mobiles, tablets or smartphones at some point in the past).

Finally, think ahead and innovate, if you don’t have internal resources to do it, partner with corporate innovation advisors, launch your own company sponsored start-up incubator, or even a venture fund with a focus on the specific AI enabling technologies, that you have identified as relevant and disruptive to your business. Many of these innovation mechanisms are already one-click away, in the cloud.

Finally, if, like me, you are interested in human intelligence as much as in AI, read on ‘Outliers: from totally dumb to the world genius directory in 48hrs’ (work in progress, to be posted later) and test yourself against the machine to get to know your IQ online, who knows, you could make it to the World Genius Directory.

 

 

Dedicated to Juan Antonio Jimenez, school mate, college & university pal, business partner and life time best friends.

Thanks to:

Martina Fernandez - inspiration & purpose - My daughter

Peter J. Simons - lateral thinking - Strategy Consultant, CEO, investment banker & friend

Jose Luis Martinez Fernandez - Technology Expert - PhD, CTO at Daedalus. 

Eric Rosenbaum - edition, curation - CNBC

Ed Fernandez is an early stage venture capitalist and board advisor to Daedalus S.A, a data analytics and predictive technology company. He is also a member of the CNBC-YPO Chief Executive Network. He can be reached in real time at @efernandez

About Daedalus - Wikipedia: http://en.wikipedia.org/wiki/User:Daedalus,_S.A./sandbox